ABSTRACT:
It is an infeasible task for psychoanalysts to monitor the chronic mental stress of patients. Due to topical and innovative developments in wireless sensor technology and physiological sensors, researchers have been attracted to pursue research in wireless body area sensor networks (WBASN) or wireless body area networks (WBSN). As a result, the wireless sensor technology has enabled medical science in enhancing real-time health monitoring and maintaining a better healthcare scenario. In our previous work, the mental stress sensor presented was able to detect the stress condition based on the Galvanic Skin Response (GSR) only with an average accuracy of about 76%. In this article, a system has been presented for sensing human mental stress by acquisition, processing and analysing the GSR and heart rate or pulse rate signals from human body. This system is proficient in detection of the mental stress condition using the GSR and heart rate signals assimilated by wearable physiological sensors in wired or wireless environments. For evaluation of the system performance, well-known supervised binary classifiers have been selected for classifying the GSR and heart rate data from the physiological dataset available and the results are discussed. This multi-sensor stress detection system has average accuracy more than the previous stress detection system using GSR.